{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import baostock as bs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "login success!\n",
      "login respond error_code:0\n",
      "login respond  error_msg:success\n"
     ]
    }
   ],
   "source": [
    "lg = bs.login()\n",
    "\n",
    "# 显示登陆返回信息\n",
    "print('login respond error_code:'+lg.error_code)\n",
    "print('login respond  error_msg:'+lg.error_msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = bs.query_history_k_data_plus(\"sh.600519\",\n",
    "    \"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST\",\n",
    "    start_date='2017-07-01', end_date='2020-6-1',\n",
    "    frequency=\"d\", adjustflag=\"3\")\n",
    "\n",
    "data_list = []\n",
    "\n",
    "while (rs.error_code == '0') & rs.next():\n",
    "    data_list.append(rs.get_row_data())\n",
    "maotai = pd.DataFrame(data_list, columns=rs.fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>474.2000</td>\n",
       "      <td>474.2000</td>\n",
       "      <td>457.9000</td>\n",
       "      <td>460.3700</td>\n",
       "      <td>471.8500</td>\n",
       "      <td>4962853</td>\n",
       "      <td>2298656464.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.395069</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.432979</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>458.9600</td>\n",
       "      <td>460.0000</td>\n",
       "      <td>449.0000</td>\n",
       "      <td>451.9200</td>\n",
       "      <td>460.3700</td>\n",
       "      <td>5648895</td>\n",
       "      <td>2563886384.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.449682</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.835476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>453.0100</td>\n",
       "      <td>460.3300</td>\n",
       "      <td>453.0100</td>\n",
       "      <td>460.1300</td>\n",
       "      <td>451.9200</td>\n",
       "      <td>3754032</td>\n",
       "      <td>1718844528.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.298841</td>\n",
       "      <td>1</td>\n",
       "      <td>1.816691</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>458.1100</td>\n",
       "      <td>460.2000</td>\n",
       "      <td>450.6000</td>\n",
       "      <td>455.9800</td>\n",
       "      <td>460.1300</td>\n",
       "      <td>4204439</td>\n",
       "      <td>1913037504.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.334696</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.901918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>451.0500</td>\n",
       "      <td>451.1000</td>\n",
       "      <td>441.2000</td>\n",
       "      <td>445.9700</td>\n",
       "      <td>449.1900</td>\n",
       "      <td>3779647</td>\n",
       "      <td>1681450720.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.300880</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.716846</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>1369.8800</td>\n",
       "      <td>1369.9500</td>\n",
       "      <td>1355.0000</td>\n",
       "      <td>1358.0000</td>\n",
       "      <td>1362.9000</td>\n",
       "      <td>2247019</td>\n",
       "      <td>3056188321.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.178900</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.359500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>1359.0000</td>\n",
       "      <td>1360.0000</td>\n",
       "      <td>1333.0000</td>\n",
       "      <td>1338.0000</td>\n",
       "      <td>1358.0000</td>\n",
       "      <td>2677741</td>\n",
       "      <td>3596654307.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.213200</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.472800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>1337.9700</td>\n",
       "      <td>1346.9400</td>\n",
       "      <td>1321.1300</td>\n",
       "      <td>1344.0000</td>\n",
       "      <td>1338.0000</td>\n",
       "      <td>2648591</td>\n",
       "      <td>3536546893.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.210800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.448400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>1332.1000</td>\n",
       "      <td>1369.9800</td>\n",
       "      <td>1332.1000</td>\n",
       "      <td>1366.6000</td>\n",
       "      <td>1344.0000</td>\n",
       "      <td>3044435</td>\n",
       "      <td>4144247222.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>1</td>\n",
       "      <td>1.681500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>sh.600519</td>\n",
       "      <td>1381.0000</td>\n",
       "      <td>1420.1000</td>\n",
       "      <td>1381.0000</td>\n",
       "      <td>1419.5000</td>\n",
       "      <td>1366.6000</td>\n",
       "      <td>3547976</td>\n",
       "      <td>4986178912.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.282400</td>\n",
       "      <td>1</td>\n",
       "      <td>3.870900</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>710 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date       code       open       high        low      close  \\\n",
       "0    2017-07-03  sh.600519   474.2000   474.2000   457.9000   460.3700   \n",
       "1    2017-07-04  sh.600519   458.9600   460.0000   449.0000   451.9200   \n",
       "2    2017-07-05  sh.600519   453.0100   460.3300   453.0100   460.1300   \n",
       "3    2017-07-06  sh.600519   458.1100   460.2000   450.6000   455.9800   \n",
       "4    2017-07-07  sh.600519   451.0500   451.1000   441.2000   445.9700   \n",
       "..          ...        ...        ...        ...        ...        ...   \n",
       "705  2020-05-26  sh.600519  1369.8800  1369.9500  1355.0000  1358.0000   \n",
       "706  2020-05-27  sh.600519  1359.0000  1360.0000  1333.0000  1338.0000   \n",
       "707  2020-05-28  sh.600519  1337.9700  1346.9400  1321.1300  1344.0000   \n",
       "708  2020-05-29  sh.600519  1332.1000  1369.9800  1332.1000  1366.6000   \n",
       "709  2020-06-01  sh.600519  1381.0000  1420.1000  1381.0000  1419.5000   \n",
       "\n",
       "      preclose   volume           amount adjustflag      turn tradestatus  \\\n",
       "0     471.8500  4962853  2298656464.0000          3  0.395069           1   \n",
       "1     460.3700  5648895  2563886384.0000          3  0.449682           1   \n",
       "2     451.9200  3754032  1718844528.0000          3  0.298841           1   \n",
       "3     460.1300  4204439  1913037504.0000          3  0.334696           1   \n",
       "4     449.1900  3779647  1681450720.0000          3  0.300880           1   \n",
       "..         ...      ...              ...        ...       ...         ...   \n",
       "705  1362.9000  2247019  3056188321.0000          3  0.178900           1   \n",
       "706  1358.0000  2677741  3596654307.0000          3  0.213200           1   \n",
       "707  1338.0000  2648591  3536546893.0000          3  0.210800           1   \n",
       "708  1344.0000  3044435  4144247222.0000          3  0.242400           1   \n",
       "709  1366.6000  3547976  4986178912.0000          3  0.282400           1   \n",
       "\n",
       "        pctChg isST  \n",
       "0    -2.432979    0  \n",
       "1    -1.835476    0  \n",
       "2     1.816691    0  \n",
       "3    -0.901918    0  \n",
       "4    -0.716846    0  \n",
       "..         ...  ...  \n",
       "705  -0.359500    0  \n",
       "706  -1.472800    0  \n",
       "707   0.448400    0  \n",
       "708   1.681500    0  \n",
       "709   3.870900    0  \n",
       "\n",
       "[710 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maotai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#参数名称\t参数描述\t算法说明\n",
    "#date\t交易所行情日期\t\n",
    "#code\t证券代码\t\n",
    "#open\t开盘价\t\n",
    "#high\t最高价\t\n",
    "#low\t最低价\t\n",
    "#close\t收盘价\t\n",
    "#preclose\t前收盘价\t见表格下方详细说明\n",
    "#volume\t成交量（累计 单位：股）\t\n",
    "#amount\t成交额（单位：人民币元）\t\n",
    "#adjustflag\t复权状态(1：后复权， 2：前复权，3：不复权）\t\n",
    "#turn\t换手率\t[指定交易日的成交量(股)/指定交易日的股票的流通股总股数(股)]*100%\n",
    "#tradestatus\t交易状态(1：正常交易 0：停牌）\t\n",
    "#pctChg\t涨跌幅（百分比）\t日涨跌幅=[(指定交易日的收盘价-指定交易日前收盘价)/指定交易日前收盘价]*100%\n",
    "#peTTM\t滚动市盈率\t(指定交易日的股票收盘价/指定交易日的每股盈余TTM)=(指定交易日的股票收盘价*截至当日公司总股本)/归属母公司股东净利润TTM\n",
    "#pbMRQ\t市净率\t(指定交易日的股票收盘价/指定交易日的每股净资产)=总市值/(最近披露的归属母公司股东的权益-其他权益工具)\n",
    "#psTTM\t滚动市销率\t(指定交易日的股票收盘价/指定交易日的每股销售额)=(指定交易日的股票收盘价*截至当日公司总股本)/营业总收入TTM\n",
    "#pcfNcfTTM\t滚动市现率\t(指定交易日的股票收盘价/指定交易日的每股现金流TTM)=(指定交易日的股票收盘价*截至当日公司总股本)/现金以及现金等价物净增加额TTM\n",
    "#isST\t是否ST股，1是，0否\t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7722f65650>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "maotai['open'].astype(float).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>36.2700</td>\n",
       "      <td>36.6000</td>\n",
       "      <td>35.7900</td>\n",
       "      <td>36.0300</td>\n",
       "      <td>36.2400</td>\n",
       "      <td>4596060</td>\n",
       "      <td>165870568.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.655382</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.579478</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>35.9500</td>\n",
       "      <td>35.9500</td>\n",
       "      <td>35.2800</td>\n",
       "      <td>35.4400</td>\n",
       "      <td>36.0300</td>\n",
       "      <td>4578062</td>\n",
       "      <td>162374417.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.648900</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.637525</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>35.3000</td>\n",
       "      <td>35.7100</td>\n",
       "      <td>35.1500</td>\n",
       "      <td>35.5800</td>\n",
       "      <td>35.4400</td>\n",
       "      <td>4885990</td>\n",
       "      <td>173146496.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.759808</td>\n",
       "      <td>1</td>\n",
       "      <td>0.395043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>35.4000</td>\n",
       "      <td>35.5800</td>\n",
       "      <td>34.9600</td>\n",
       "      <td>35.3100</td>\n",
       "      <td>35.5800</td>\n",
       "      <td>5848700</td>\n",
       "      <td>206319019.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.106551</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.758854</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>35.1500</td>\n",
       "      <td>35.2500</td>\n",
       "      <td>34.9500</td>\n",
       "      <td>35.1200</td>\n",
       "      <td>35.3100</td>\n",
       "      <td>3989411</td>\n",
       "      <td>139921297.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>1.436883</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.538098</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>28.8200</td>\n",
       "      <td>29.5900</td>\n",
       "      <td>28.4700</td>\n",
       "      <td>29.4300</td>\n",
       "      <td>28.6500</td>\n",
       "      <td>10627962</td>\n",
       "      <td>309809333.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.827900</td>\n",
       "      <td>1</td>\n",
       "      <td>2.722500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>29.3000</td>\n",
       "      <td>29.4800</td>\n",
       "      <td>28.7800</td>\n",
       "      <td>28.8100</td>\n",
       "      <td>29.4300</td>\n",
       "      <td>5705753</td>\n",
       "      <td>165702296.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.055100</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.106700</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>28.7000</td>\n",
       "      <td>28.9400</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>28.3900</td>\n",
       "      <td>28.8100</td>\n",
       "      <td>6638834</td>\n",
       "      <td>188666305.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.391100</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.457800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>28.4000</td>\n",
       "      <td>31.2300</td>\n",
       "      <td>28.3000</td>\n",
       "      <td>31.1500</td>\n",
       "      <td>28.3900</td>\n",
       "      <td>28517387</td>\n",
       "      <td>863932038.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10.271200</td>\n",
       "      <td>1</td>\n",
       "      <td>9.721700</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>sh.600511</td>\n",
       "      <td>33.4600</td>\n",
       "      <td>33.4600</td>\n",
       "      <td>32.3900</td>\n",
       "      <td>32.6700</td>\n",
       "      <td>31.1500</td>\n",
       "      <td>41664597</td>\n",
       "      <td>1372109171.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>15.006500</td>\n",
       "      <td>1</td>\n",
       "      <td>4.879600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>710 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date       code     open     high      low    close preclose  \\\n",
       "0    2017-07-03  sh.600511  36.2700  36.6000  35.7900  36.0300  36.2400   \n",
       "1    2017-07-04  sh.600511  35.9500  35.9500  35.2800  35.4400  36.0300   \n",
       "2    2017-07-05  sh.600511  35.3000  35.7100  35.1500  35.5800  35.4400   \n",
       "3    2017-07-06  sh.600511  35.4000  35.5800  34.9600  35.3100  35.5800   \n",
       "4    2017-07-07  sh.600511  35.1500  35.2500  34.9500  35.1200  35.3100   \n",
       "..          ...        ...      ...      ...      ...      ...      ...   \n",
       "705  2020-05-26  sh.600511  28.8200  29.5900  28.4700  29.4300  28.6500   \n",
       "706  2020-05-27  sh.600511  29.3000  29.4800  28.7800  28.8100  29.4300   \n",
       "707  2020-05-28  sh.600511  28.7000  28.9400  28.0000  28.3900  28.8100   \n",
       "708  2020-05-29  sh.600511  28.4000  31.2300  28.3000  31.1500  28.3900   \n",
       "709  2020-06-01  sh.600511  33.4600  33.4600  32.3900  32.6700  31.1500   \n",
       "\n",
       "       volume           amount adjustflag       turn tradestatus     pctChg  \\\n",
       "0     4596060   165870568.0000          3   1.655382           1  -0.579478   \n",
       "1     4578062   162374417.0000          3   1.648900           1  -1.637525   \n",
       "2     4885990   173146496.0000          3   1.759808           1   0.395043   \n",
       "3     5848700   206319019.0000          3   2.106551           1  -0.758854   \n",
       "4     3989411   139921297.0000          3   1.436883           1  -0.538098   \n",
       "..        ...              ...        ...        ...         ...        ...   \n",
       "705  10627962   309809333.0000          3   3.827900           1   2.722500   \n",
       "706   5705753   165702296.0000          3   2.055100           1  -2.106700   \n",
       "707   6638834   188666305.0000          3   2.391100           1  -1.457800   \n",
       "708  28517387   863932038.0000          3  10.271200           1   9.721700   \n",
       "709  41664597  1372109171.0000          3  15.006500           1   4.879600   \n",
       "\n",
       "    isST  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  \n",
       "..   ...  \n",
       "705    0  \n",
       "706    0  \n",
       "707    0  \n",
       "708    0  \n",
       "709    0  \n",
       "\n",
       "[710 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = bs.query_history_k_data_plus(\"sh.600511\",\n",
    "    \"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST\",\n",
    "    start_date='2017-07-01', end_date='2020-6-1',\n",
    "    frequency=\"d\", adjustflag=\"3\")\n",
    "\n",
    "data_list = []\n",
    "\n",
    "while (rs.error_code == '0') & rs.next():\n",
    "    data_list.append(rs.get_row_data())\n",
    "guoyao = pd.DataFrame(data_list, columns=rs.fields)\n",
    "guoyao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f772265b910>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "maotai['open'].astype(float).plot()\n",
    "guoyao['open'].astype(float).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7722527a90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "guoyao['c-p'] = guoyao['close'].astype(float) - guoyao['preclose'].astype(float)\n",
    "guoyao['c-p'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wistbean/anaconda3/lib/python3.7/site-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
    }
   ],
   "source": [
    "import pandas_datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2005-01-01'\n",
    "end_date = '2020-06-01'\n",
    "ticker = 'AAPL'\n",
    "data = pandas_datareader.get_data_yahoo(ticker, start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>4.642857</td>\n",
       "      <td>4.573571</td>\n",
       "      <td>4.635000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>69647200.0</td>\n",
       "      <td>3.982207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-03</th>\n",
       "      <td>4.650714</td>\n",
       "      <td>4.471428</td>\n",
       "      <td>4.627143</td>\n",
       "      <td>4.520714</td>\n",
       "      <td>172998000.0</td>\n",
       "      <td>3.913571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>4.676429</td>\n",
       "      <td>4.497857</td>\n",
       "      <td>4.556428</td>\n",
       "      <td>4.567143</td>\n",
       "      <td>274202600.0</td>\n",
       "      <td>3.953765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05</th>\n",
       "      <td>4.660714</td>\n",
       "      <td>4.575000</td>\n",
       "      <td>4.604286</td>\n",
       "      <td>4.607143</td>\n",
       "      <td>170108400.0</td>\n",
       "      <td>3.988392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06</th>\n",
       "      <td>4.636428</td>\n",
       "      <td>4.523571</td>\n",
       "      <td>4.619286</td>\n",
       "      <td>4.610714</td>\n",
       "      <td>176388800.0</td>\n",
       "      <td>3.991483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-26</th>\n",
       "      <td>324.239990</td>\n",
       "      <td>316.500000</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>316.730011</td>\n",
       "      <td>31380500.0</td>\n",
       "      <td>316.730011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-27</th>\n",
       "      <td>318.709991</td>\n",
       "      <td>313.089996</td>\n",
       "      <td>316.140015</td>\n",
       "      <td>318.109985</td>\n",
       "      <td>28236300.0</td>\n",
       "      <td>318.109985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-28</th>\n",
       "      <td>323.440002</td>\n",
       "      <td>315.630005</td>\n",
       "      <td>316.769989</td>\n",
       "      <td>318.250000</td>\n",
       "      <td>33390200.0</td>\n",
       "      <td>318.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>321.149994</td>\n",
       "      <td>316.470001</td>\n",
       "      <td>319.250000</td>\n",
       "      <td>317.940002</td>\n",
       "      <td>38399500.0</td>\n",
       "      <td>317.940002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>322.350006</td>\n",
       "      <td>317.209991</td>\n",
       "      <td>317.750000</td>\n",
       "      <td>321.850006</td>\n",
       "      <td>20197800.0</td>\n",
       "      <td>321.850006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3880 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close       Volume  \\\n",
       "Date                                                                      \n",
       "2004-12-31    4.642857    4.573571    4.635000    4.600000   69647200.0   \n",
       "2005-01-03    4.650714    4.471428    4.627143    4.520714  172998000.0   \n",
       "2005-01-04    4.676429    4.497857    4.556428    4.567143  274202600.0   \n",
       "2005-01-05    4.660714    4.575000    4.604286    4.607143  170108400.0   \n",
       "2005-01-06    4.636428    4.523571    4.619286    4.610714  176388800.0   \n",
       "...                ...         ...         ...         ...          ...   \n",
       "2020-05-26  324.239990  316.500000  323.500000  316.730011   31380500.0   \n",
       "2020-05-27  318.709991  313.089996  316.140015  318.109985   28236300.0   \n",
       "2020-05-28  323.440002  315.630005  316.769989  318.250000   33390200.0   \n",
       "2020-05-29  321.149994  316.470001  319.250000  317.940002   38399500.0   \n",
       "2020-06-01  322.350006  317.209991  317.750000  321.850006   20197800.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2004-12-31    3.982207  \n",
       "2005-01-03    3.913571  \n",
       "2005-01-04    3.953765  \n",
       "2005-01-05    3.988392  \n",
       "2005-01-06    3.991483  \n",
       "...                ...  \n",
       "2020-05-26  316.730011  \n",
       "2020-05-27  318.109985  \n",
       "2020-05-28  318.250000  \n",
       "2020-05-29  317.940002  \n",
       "2020-06-01  321.850006  \n",
       "\n",
       "[3880 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f771fdfa310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Attributes</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Adj Close</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Close</th>\n",
       "      <th colspan=\"3\" halign=\"left\">High</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Low</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>3.982207</td>\n",
       "      <td>44.290001</td>\n",
       "      <td>96.035034</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>44.290001</td>\n",
       "      <td>96.035034</td>\n",
       "      <td>4.642857</td>\n",
       "      <td>45.490002</td>\n",
       "      <td>99.566803</td>\n",
       "      <td>4.573571</td>\n",
       "      <td>44.160000</td>\n",
       "      <td>95.920464</td>\n",
       "      <td>4.635000</td>\n",
       "      <td>45.130001</td>\n",
       "      <td>99.243011</td>\n",
       "      <td>69647200.0</td>\n",
       "      <td>4790700.0</td>\n",
       "      <td>15394400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-03</th>\n",
       "      <td>3.913571</td>\n",
       "      <td>44.520000</td>\n",
       "      <td>100.976517</td>\n",
       "      <td>4.520714</td>\n",
       "      <td>44.520000</td>\n",
       "      <td>100.976517</td>\n",
       "      <td>4.650714</td>\n",
       "      <td>45.439999</td>\n",
       "      <td>101.439781</td>\n",
       "      <td>4.471428</td>\n",
       "      <td>44.209999</td>\n",
       "      <td>97.365051</td>\n",
       "      <td>4.627143</td>\n",
       "      <td>44.950001</td>\n",
       "      <td>98.331429</td>\n",
       "      <td>172998000.0</td>\n",
       "      <td>10446500.0</td>\n",
       "      <td>31807000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>3.953765</td>\n",
       "      <td>42.139999</td>\n",
       "      <td>96.886841</td>\n",
       "      <td>4.567143</td>\n",
       "      <td>42.139999</td>\n",
       "      <td>96.886841</td>\n",
       "      <td>4.676429</td>\n",
       "      <td>43.259998</td>\n",
       "      <td>101.086105</td>\n",
       "      <td>4.497857</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>96.378746</td>\n",
       "      <td>4.556428</td>\n",
       "      <td>42.669998</td>\n",
       "      <td>100.323959</td>\n",
       "      <td>274202600.0</td>\n",
       "      <td>19418500.0</td>\n",
       "      <td>27614900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05</th>\n",
       "      <td>3.988392</td>\n",
       "      <td>41.770000</td>\n",
       "      <td>96.393692</td>\n",
       "      <td>4.607143</td>\n",
       "      <td>41.770000</td>\n",
       "      <td>96.393692</td>\n",
       "      <td>4.660714</td>\n",
       "      <td>42.759998</td>\n",
       "      <td>98.082367</td>\n",
       "      <td>4.575000</td>\n",
       "      <td>41.560001</td>\n",
       "      <td>95.756081</td>\n",
       "      <td>4.604286</td>\n",
       "      <td>41.570000</td>\n",
       "      <td>96.363808</td>\n",
       "      <td>170108400.0</td>\n",
       "      <td>8354200.0</td>\n",
       "      <td>16534800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06</th>\n",
       "      <td>3.991483</td>\n",
       "      <td>41.049999</td>\n",
       "      <td>93.922951</td>\n",
       "      <td>4.610714</td>\n",
       "      <td>41.049999</td>\n",
       "      <td>93.922951</td>\n",
       "      <td>4.636428</td>\n",
       "      <td>42.250000</td>\n",
       "      <td>97.584229</td>\n",
       "      <td>4.523571</td>\n",
       "      <td>40.900002</td>\n",
       "      <td>93.509506</td>\n",
       "      <td>4.619286</td>\n",
       "      <td>41.810001</td>\n",
       "      <td>97.175758</td>\n",
       "      <td>176388800.0</td>\n",
       "      <td>8700900.0</td>\n",
       "      <td>20851900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-26</th>\n",
       "      <td>316.730011</td>\n",
       "      <td>2421.860107</td>\n",
       "      <td>1417.020020</td>\n",
       "      <td>316.730011</td>\n",
       "      <td>2421.860107</td>\n",
       "      <td>1417.020020</td>\n",
       "      <td>324.239990</td>\n",
       "      <td>2462.000000</td>\n",
       "      <td>1441.000000</td>\n",
       "      <td>316.500000</td>\n",
       "      <td>2414.060059</td>\n",
       "      <td>1412.130005</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>2458.000000</td>\n",
       "      <td>1437.270020</td>\n",
       "      <td>31380500.0</td>\n",
       "      <td>3568200.0</td>\n",
       "      <td>2060600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-27</th>\n",
       "      <td>318.109985</td>\n",
       "      <td>2410.389893</td>\n",
       "      <td>1417.839966</td>\n",
       "      <td>318.109985</td>\n",
       "      <td>2410.389893</td>\n",
       "      <td>1417.839966</td>\n",
       "      <td>318.709991</td>\n",
       "      <td>2413.580078</td>\n",
       "      <td>1421.739990</td>\n",
       "      <td>313.089996</td>\n",
       "      <td>2330.000000</td>\n",
       "      <td>1391.290039</td>\n",
       "      <td>316.140015</td>\n",
       "      <td>2404.989990</td>\n",
       "      <td>1417.250000</td>\n",
       "      <td>28236300.0</td>\n",
       "      <td>5056900.0</td>\n",
       "      <td>1685800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-28</th>\n",
       "      <td>318.250000</td>\n",
       "      <td>2401.100098</td>\n",
       "      <td>1416.729980</td>\n",
       "      <td>318.250000</td>\n",
       "      <td>2401.100098</td>\n",
       "      <td>1416.729980</td>\n",
       "      <td>323.440002</td>\n",
       "      <td>2436.969971</td>\n",
       "      <td>1440.839966</td>\n",
       "      <td>315.630005</td>\n",
       "      <td>2378.229980</td>\n",
       "      <td>1396.000000</td>\n",
       "      <td>316.769989</td>\n",
       "      <td>2384.330078</td>\n",
       "      <td>1396.859985</td>\n",
       "      <td>33390200.0</td>\n",
       "      <td>3190200.0</td>\n",
       "      <td>1692200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>317.940002</td>\n",
       "      <td>2442.370117</td>\n",
       "      <td>1428.920044</td>\n",
       "      <td>317.940002</td>\n",
       "      <td>2442.370117</td>\n",
       "      <td>1428.920044</td>\n",
       "      <td>321.149994</td>\n",
       "      <td>2442.370117</td>\n",
       "      <td>1432.569946</td>\n",
       "      <td>316.470001</td>\n",
       "      <td>2398.199951</td>\n",
       "      <td>1413.349976</td>\n",
       "      <td>319.250000</td>\n",
       "      <td>2415.939941</td>\n",
       "      <td>1416.939941</td>\n",
       "      <td>38399500.0</td>\n",
       "      <td>3529300.0</td>\n",
       "      <td>1838100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>321.850006</td>\n",
       "      <td>2471.040039</td>\n",
       "      <td>1431.819946</td>\n",
       "      <td>321.850006</td>\n",
       "      <td>2471.040039</td>\n",
       "      <td>1431.819946</td>\n",
       "      <td>322.350006</td>\n",
       "      <td>2476.929932</td>\n",
       "      <td>1437.959961</td>\n",
       "      <td>317.209991</td>\n",
       "      <td>2444.169922</td>\n",
       "      <td>1418.000000</td>\n",
       "      <td>317.750000</td>\n",
       "      <td>2448.000000</td>\n",
       "      <td>1418.390015</td>\n",
       "      <td>20197800.0</td>\n",
       "      <td>2928900.0</td>\n",
       "      <td>1217100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3880 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Attributes   Adj Close                                 Close               \\\n",
       "Symbols           AAPL         AMZN         GOOG        AAPL         AMZN   \n",
       "Date                                                                        \n",
       "2004-12-31    3.982207    44.290001    96.035034    4.600000    44.290001   \n",
       "2005-01-03    3.913571    44.520000   100.976517    4.520714    44.520000   \n",
       "2005-01-04    3.953765    42.139999    96.886841    4.567143    42.139999   \n",
       "2005-01-05    3.988392    41.770000    96.393692    4.607143    41.770000   \n",
       "2005-01-06    3.991483    41.049999    93.922951    4.610714    41.049999   \n",
       "...                ...          ...          ...         ...          ...   \n",
       "2020-05-26  316.730011  2421.860107  1417.020020  316.730011  2421.860107   \n",
       "2020-05-27  318.109985  2410.389893  1417.839966  318.109985  2410.389893   \n",
       "2020-05-28  318.250000  2401.100098  1416.729980  318.250000  2401.100098   \n",
       "2020-05-29  317.940002  2442.370117  1428.920044  317.940002  2442.370117   \n",
       "2020-06-01  321.850006  2471.040039  1431.819946  321.850006  2471.040039   \n",
       "\n",
       "Attributes                     High                                   Low  \\\n",
       "Symbols            GOOG        AAPL         AMZN         GOOG        AAPL   \n",
       "Date                                                                        \n",
       "2004-12-31    96.035034    4.642857    45.490002    99.566803    4.573571   \n",
       "2005-01-03   100.976517    4.650714    45.439999   101.439781    4.471428   \n",
       "2005-01-04    96.886841    4.676429    43.259998   101.086105    4.497857   \n",
       "2005-01-05    96.393692    4.660714    42.759998    98.082367    4.575000   \n",
       "2005-01-06    93.922951    4.636428    42.250000    97.584229    4.523571   \n",
       "...                 ...         ...          ...          ...         ...   \n",
       "2020-05-26  1417.020020  324.239990  2462.000000  1441.000000  316.500000   \n",
       "2020-05-27  1417.839966  318.709991  2413.580078  1421.739990  313.089996   \n",
       "2020-05-28  1416.729980  323.440002  2436.969971  1440.839966  315.630005   \n",
       "2020-05-29  1428.920044  321.149994  2442.370117  1432.569946  316.470001   \n",
       "2020-06-01  1431.819946  322.350006  2476.929932  1437.959961  317.209991   \n",
       "\n",
       "Attributes                                  Open                            \\\n",
       "Symbols            AMZN         GOOG        AAPL         AMZN         GOOG   \n",
       "Date                                                                         \n",
       "2004-12-31    44.160000    95.920464    4.635000    45.130001    99.243011   \n",
       "2005-01-03    44.209999    97.365051    4.627143    44.950001    98.331429   \n",
       "2005-01-04    41.500000    96.378746    4.556428    42.669998   100.323959   \n",
       "2005-01-05    41.560001    95.756081    4.604286    41.570000    96.363808   \n",
       "2005-01-06    40.900002    93.509506    4.619286    41.810001    97.175758   \n",
       "...                 ...          ...         ...          ...          ...   \n",
       "2020-05-26  2414.060059  1412.130005  323.500000  2458.000000  1437.270020   \n",
       "2020-05-27  2330.000000  1391.290039  316.140015  2404.989990  1417.250000   \n",
       "2020-05-28  2378.229980  1396.000000  316.769989  2384.330078  1396.859985   \n",
       "2020-05-29  2398.199951  1413.349976  319.250000  2415.939941  1416.939941   \n",
       "2020-06-01  2444.169922  1418.000000  317.750000  2448.000000  1418.390015   \n",
       "\n",
       "Attributes       Volume                          \n",
       "Symbols            AAPL        AMZN        GOOG  \n",
       "Date                                             \n",
       "2004-12-31   69647200.0   4790700.0  15394400.0  \n",
       "2005-01-03  172998000.0  10446500.0  31807000.0  \n",
       "2005-01-04  274202600.0  19418500.0  27614900.0  \n",
       "2005-01-05  170108400.0   8354200.0  16534800.0  \n",
       "2005-01-06  176388800.0   8700900.0  20851900.0  \n",
       "...                 ...         ...         ...  \n",
       "2020-05-26   31380500.0   3568200.0   2060600.0  \n",
       "2020-05-27   28236300.0   5056900.0   1685800.0  \n",
       "2020-05-28   33390200.0   3190200.0   1692200.0  \n",
       "2020-05-29   38399500.0   3529300.0   1838100.0  \n",
       "2020-06-01   20197800.0   2928900.0   1217100.0  \n",
       "\n",
       "[3880 rows x 18 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coms = ['AAPL', 'AMZN', 'GOOG']\n",
    "coms_df = pandas_datareader.get_data_yahoo(coms, start_date, end_date)\n",
    "coms_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>44.290001</td>\n",
       "      <td>96.035034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-03</th>\n",
       "      <td>4.520714</td>\n",
       "      <td>44.520000</td>\n",
       "      <td>100.976517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>4.567143</td>\n",
       "      <td>42.139999</td>\n",
       "      <td>96.886841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05</th>\n",
       "      <td>4.607143</td>\n",
       "      <td>41.770000</td>\n",
       "      <td>96.393692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06</th>\n",
       "      <td>4.610714</td>\n",
       "      <td>41.049999</td>\n",
       "      <td>93.922951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols         AAPL       AMZN        GOOG\n",
       "Date                                       \n",
       "2004-12-31  4.600000  44.290001   96.035034\n",
       "2005-01-03  4.520714  44.520000  100.976517\n",
       "2005-01-04  4.567143  42.139999   96.886841\n",
       "2005-01-05  4.607143  41.770000   96.393692\n",
       "2005-01-06  4.610714  41.049999   93.922951"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = coms_df['Close']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f771f73fe90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Close'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Close'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-a5983cf9869a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mday_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpct_change\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mday_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Close'"
     ]
    }
   ],
   "source": [
    "day_df = df['Close'].pct_change()\n",
    "day_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f771fb17450>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
